In this paper we propose a novel procedure for urban road network extraction in high resolution SAR images. It is based on a multi-scale detection step including fusion of multiple features aimed at considering spatial high resolution as well as the spectral characteristics of the SAR images. Advantages over existing and previous extraction procedures are proved by comparison using data from different sensors and different test areas. I. INTRODUCTION High resolution SAR by Low Earth Orbit (LEO) satellites is going to have a deep impact on remote sensing data availability both because of the very short time between acquisitions and the spatial resolution, fine enough to monitor artificial structures. In turn, this will require more precise and efficient algorithms for the interpretation of this kind of SAR data. In fact, at high and very high resolution, natural and artificial objects must be individuated exploiting both their geometrical and spectral features, which show peculiar behaviors in SAR data. Following this idea, in this work we develop a decision fu- sion approach based on different detectors specifically tailored for road extraction from high resolution SAR data of urban areas. The approach exploits geometric a priori knowledge as well as spectral information about road materials. Street and roads in coarse SAR images may appear as dark or bright features, depending on their orientation. This is less true for high resolution SAR images, where roads are more-than-one-pixel wide: they most likely appear as dark, elongated areas, possibly with very bright sides. As a result, we may approach their detection and extraction by using geometrical analysis (1), looking for long edges, or by exploiting simpler multiple thresholding approaches (2), searching for dark, homogeneous areas. The proposed algorithm integrates both these approaches into a multi-scale feature fusion framework. Road candidate extraction in high resolution imagery usu- ally starts with road area detection, which of course may be obtained in optical images by looking for the spectral response of road materials. However, road class recognition in high resolution SAR data would imply complex segmentation algorithms based on data statistics. In this paper it is preferred to prove that multiple detectors may be enough to obtain good results. Therefore, in this paper a new extraction method is proposed, based on multiple feature detection and fusion , designed to be as automatic as possible and aimed at optimal junction preservation. The algorithm exploits spatial